Core

Deduplication and LSH

Exact duplicates fall out of hashing the normalized content, but near-duplicates (the same record with a typo, a reordered address, boilerplate that repeats across documents) need similarity, not equality. MinHash estimates Jaccard similarity cheaply, and Locality-Sensitive Hashing buckets similar signatures so you only compare likely pairs instead of all O(n^2). This is a constant reflex when merging messy enterprise data and when curating training corpora.

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RELATED CONCEPTS

Data Quality and Validation Parsing Messy, Real-World Data Embeddings & Vector Representations

LESSONS THAT TEACH THIS

Foundations of Forward Deployed EngineeringProfile the data and turn uncertainty into a scoped plan28 min→Foundations of Forward Deployed EngineeringResolve identities without hiding uncertain or unmatched records30 min→The FDE EngagementKeep link evidence, uncertainty and history in the query30 min→

PRACTICE THIS IN REAL QUESTIONS

SQL & Data EngineeringA table has duplicate rows for the same business key. Write SQL to keep only the latest version of each.→Coding & DSAParse server logs and report the top-K users by error count per hour, handling malformed lines→Coding & DSASessionize a support-event stream: a gap over 30 minutes starts a new session; dedupe events→Coding & DSABuild a CSV diff tool: report added, removed, and changed rows between two files, with composite keys→Behavioral & Customer ScenariosMid-deployment, the data is far dirtier than scoped and the timeline is now impossible. Draft the conversation with the exec sponsor.→Coding & DSAFuzzy-match entities across two customer lists: normalization, edit distance, and scaling past O(n×m)→